Analysis of the 10x samples. - tSNE plots - Cell cycle regression - PCA - Alignment - Marker gene expression - tSNE colored on metadata
library(Seurat)
## Loading required package: ggplot2
## Loading required package: cowplot
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## Attaching package: 'cowplot'
## The following object is masked from 'package:ggplot2':
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## ggsave
## Loading required package: Matrix
library(ggplot2)
library(dplyr)
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## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
all10x <- readRDS('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/data/10x')
Below are several tSNE plots of the 10x data. tSNE was performed on the first 15 principal components of the log-normalized scaled (nUMI and percent.mito regressed out) data.
Visceral and perirenal seem a bit mixed, and supraclavicular and subcutaneous too.
TSNEPlot(all10x, pt.size=0.1, group.by='sample_name', do.label=T)
tSNE plots of samples within their subtissue. Peri2 and Peri3 seem to overlap really well, as well as Supra1 and Supra2, and Visce1 and Visce3.
subtissues <- all10x@meta.data$sample_name2
t1 <- TSNEPlot(all10x, cells.use=all10x@cell.names[which(subtissues %in% 'Peri')], group.by='sample_name', pt.size=0.2)$plot
t2 <- TSNEPlot(all10x, cells.use=all10x@cell.names[which(subtissues %in% 'Subq')], group.by='sample_name', pt.size=0.2)$plot
t3 <- TSNEPlot(all10x, cells.use=all10x@cell.names[which(subtissues %in% 'Supra')], group.by='sample_name', pt.size=0.2)$plot
t4 <- TSNEPlot(all10x, cells.use=all10x@cell.names[which(subtissues %in% 'Visce')], group.by='sample_name', pt.size=0.2)$plot
#plot_grid(t1, t2, t3, t4)
tSNE colored on subtissue.
TSNEPlot(all10x, group.by='sample_name2', pt.size=0.1)
tSNE colored by cell cycle phase.
TSNEPlot(all10x, group.by='Phase', pt.size=0.1)
Some clustering with different resolutions. res=0.5
TSNEPlot(all10x, pt.size=0.1, group.by='res.0.5', do.label=T)
res=0.6
TSNEPlot(all10x, pt.size=0.1, group.by='res.0.6')
res=0.7
TSNEPlot(all10x, pt.size=0.1, group.by='res.0.7', do.label=T)
res=0.8
TSNEPlot(all10x, pt.size=0.1, group.by='res.0.8', do.label=T)
res=0.9
TSNEPlot(all10x, pt.size=0.1, group.by='res.0.9', do.label=T)
res=1
TSNEPlot(all10x, pt.size=0.1, group.by='res.1', do.label=T)
all10x.ccregout <- readRDS('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/data/10x-ccregout')
tSNE of the cell cycle regressed out. There does not seem to be a lot of structure within samples now.
TSNEPlot(all10x.ccregout, pt.size=0.1, group.by='sample_name')
TSNEPlot(all10x.ccregout, pt.size=0.1, group.by='Phase')
Subtissues
subtissues <- all10x.ccregout@meta.data$sample_name2
t1 <- TSNEPlot(all10x.ccregout, cells.use=all10x.ccregout@cell.names[which(subtissues %in% 'Peri')], group.by='sample_name', pt.size=0.2)$plot
t2 <- TSNEPlot(all10x.ccregout, cells.use=all10x.ccregout@cell.names[which(subtissues %in% 'Subq')], group.by='sample_name', pt.size=0.2)$plot
t3 <- TSNEPlot(all10x.ccregout, cells.use=all10x.ccregout@cell.names[which(subtissues %in% 'Supra')], group.by='sample_name', pt.size=0.2)$plot
t4 <- TSNEPlot(all10x.ccregout, cells.use=all10x.ccregout@cell.names[which(subtissues %in% 'Visce')], group.by='sample_name', pt.size=0.2)$plot
#plot_grid(t1, t2, t3, t4)
Some PCA plots. PC1 seems to capture cell cycle effects, and PC2 seems to capture some of the sample variability.
PCAPlot(all10x, group.by='Phase', pt.size=0.1)
PCAPlot(all10x, group.by='sample_name', pt.size=0.1)